CN114175048A - Anti-counterfeiting product with random micro-point characteristic and manufacturing method and verification method thereof - Google Patents

Anti-counterfeiting product with random micro-point characteristic and manufacturing method and verification method thereof Download PDF

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CN114175048A
CN114175048A CN201980098985.7A CN201980098985A CN114175048A CN 114175048 A CN114175048 A CN 114175048A CN 201980098985 A CN201980098985 A CN 201980098985A CN 114175048 A CN114175048 A CN 114175048A
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micro
product
distribution
point
feature
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谢晖
高煜
闫钰龙
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Robert Bosch GmbH
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Robert Bosch GmbH
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K19/00—Record carriers for use with machines and with at least a part designed to carry digital markings
    • G06K19/06—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
    • G06K19/06009—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code with optically detectable marking
    • G06K19/06046—Constructional details
    • G06K19/06084—Constructional details the marking being based on nanoparticles or microbeads
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B42—BOOKBINDING; ALBUMS; FILES; SPECIAL PRINTED MATTER
    • B42D—BOOKS; BOOK COVERS; LOOSE LEAVES; PRINTED MATTER CHARACTERISED BY IDENTIFICATION OR SECURITY FEATURES; PRINTED MATTER OF SPECIAL FORMAT OR STYLE NOT OTHERWISE PROVIDED FOR; DEVICES FOR USE THEREWITH AND NOT OTHERWISE PROVIDED FOR; MOVABLE-STRIP WRITING OR READING APPARATUS
    • B42D25/00—Information-bearing cards or sheet-like structures characterised by identification or security features; Manufacture thereof
    • B42D25/30—Identification or security features, e.g. for preventing forgery
    • B42D25/305—Associated digital information
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K19/00—Record carriers for use with machines and with at least a part designed to carry digital markings
    • G06K19/06—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code
    • G06K19/08—Record carriers for use with machines and with at least a part designed to carry digital markings characterised by the kind of the digital marking, e.g. shape, nature, code using markings of different kinds or more than one marking of the same kind in the same record carrier, e.g. one marking being sensed by optical and the other by magnetic means
    • G06K19/083—Constructional details
    • G06K19/086—Constructional details with markings consisting of randomly placed or oriented elements, the randomness of the elements being useable for generating a unique identifying signature of the record carrier, e.g. randomly placed magnetic fibers or magnetic particles in the body of a credit card

Abstract

The invention relates to an anti-counterfeiting product with random micro-point characteristics, and a manufacturing method and a verification method thereof. The anti-counterfeiting product comprises a product mark, and the surface of the product mark is distributed with micro-point characteristics which are randomly distributed. The manufacturing method of the anti-counterfeiting product comprises the following steps: generating randomly distributed micro-point features; generating a digital product identification of the product; embedding the micro-point characteristics into a digital product identification of the product; and printing the digital product identification embedded with the micro-point characteristics on the surface of the product.

Description

Anti-counterfeiting product with random micro-point characteristic and manufacturing method and verification method thereof Technical Field
The invention relates to an anti-counterfeiting product and a manufacturing method and a verification method thereof.
Background
Counterfeit products cause significant losses to both the producer and the consumer and therefore need to be controlled by the use of secure and reliable anti-counterfeiting techniques. The existing anti-counterfeiting technology aiming at products comprises the following steps:
the digital anti-counterfeiting technology utilizes a bar code or a two-dimensional code to provide a unique Identification (ID) for a product for anti-counterfeiting verification and traceability, is easy to copy and has poor safety.
The texture anti-counterfeiting technology uses randomly generated natural textures as anti-counterfeiting characteristics, and the textures are not physically reproducible and have unreproducible characteristics; however, the existing texture anti-counterfeiting technology lacks the automatic identification capability of anti-counterfeiting features, which requires visual identification of the anti-counterfeiting features, or relies on adding fiber materials in the production process to form the anti-counterfeiting features, thereby resulting in increased cost of anti-counterfeiting products and inconvenience in production.
Disclosure of Invention
The invention aims to provide an anti-counterfeiting product which has non-reproducible anti-counterfeiting characteristics and can reduce the production cost, and a manufacturing method and an identification method thereof, so as to overcome the problems in the prior art.
The invention provides an anti-counterfeiting product, which comprises a product mark; wherein, the surface of the product mark is distributed with micro-point characteristics which are distributed randomly.
According to an embodiment of the present invention, the product identification includes at least one of a bar code and a two-dimensional graphic code.
According to an embodiment of the invention, the micro-points of the micro-point features have at least one of a predetermined shape feature, a position feature, a color feature.
According to an embodiment of the invention, the size of each micro-dot in the micro-dot feature is set to be in the range of 50 to 90 micrometers.
The invention also provides a manufacturing method of the anti-counterfeiting product, which comprises the following steps: generating randomly distributed micro-point features; generating a digital product identification for the product; embedding the micro-point features into a digital product identification of the product; printing the digital product identification embedded with the micro-point features on the surface of the product.
According to an embodiment of the invention, the micro-points of the micro-point features have at least one of a predetermined shape feature, a position feature, a color feature.
According to an embodiment of the present invention, generating randomly distributed micro-point features comprises: appointing a random distribution function and obtaining a probability density function PDF of the random distribution function; calculating a cumulative distribution function CDF of PDF; carrying out inverse transformation on the CDF to obtain an inverse function CDF-1 of the CDF; generating uniformly distributed random numbers U; and substituting U into CDF-1 to obtain the random distribution of the micro-point characteristics.
According to an embodiment of the present invention, the random distribution of the micro-point features comprises a joint distribution of at least one of a uniform distribution, a gaussian distribution, a skewed gaussian distribution, an exponential distribution, or any combination thereof.
According to the embodiment of the invention, the manufacturing method of the anti-counterfeiting product further comprises the following steps: and sampling the obtained distribution of the micro-point features, and generating the micro-point features with unique identification for each product or product identification.
According to an embodiment of the present invention, embedding the micro-point feature into a digital product identification of the product comprises: embedding the micro-point characteristics into a digital product identification of the product according to avoidance rules; wherein the avoidance rules limit the location distribution and color distribution of the micro-dots to ensure that the micro-dots do not affect the normal reading of the product identification.
In some embodiments of the present invention, the method of making a counterfeit-resistant product further comprises: the generated micro-point features for each product are saved for product verification.
The invention also provides a verification method of the anti-counterfeiting product, which comprises the following steps: acquiring an image of the product, and finding an area where a product identifier of the product is located from the acquired image; reading the micro-point features in the region using image processing techniques; extracting the micro-point characteristics which are pre-stored and generated aiming at the product; and comparing the read micro-point characteristics with pre-stored micro-point characteristics generated aiming at the product to verify the authenticity of the product.
According to an embodiment of the invention, the comparing step comprises: and judging whether at least one of the micro shape, the position distribution and the color distribution of the read micro point features is consistent with the micro point features which are stored in advance and are generated aiming at the product.
According to the anti-counterfeiting product provided by the embodiment of the invention, the technical effects include but are not limited to: by adopting the combination of the micro-point characteristics and the product identification, the conventional traceable function of the product identification is saved, and the micro-point characteristics are added as unreproducible anti-counterfeiting characteristics, so that the safety of an anti-counterfeiting system is improved; meanwhile, as the micro-point features are randomly distributed on the surface of the product identifier, the micro-point features are easy to generate in the printing process of the product identifier, thereby simplifying the production process of the anti-counterfeiting product and saving the cost.
Drawings
FIG. 1 is a flow chart of a method of making a security product according to an embodiment of the invention;
FIG. 2 is a schematic illustration of embedding a micro-dot feature into a product two-dimensional code in an embodiment;
fig. 3(a) to 3(h) are examples of shape vectors of micro-points in the embodiment;
FIGS. 4(a) and 4(b) show images of probability density functions when a uniform distribution function is used as a random distribution function of micro-points, and distribution maps of micro-points sampled from the random distribution;
FIGS. 5(a) and 5(b) show images of probability density functions when a one-dimensional Gaussian distribution function is used as a random distribution function of micro-points, and distribution maps of micro-points sampled from the random distribution;
FIGS. 6(a) and 6(b) show images of probability density functions when a two-dimensional Gaussian distribution function is used as a random distribution function of micro-points, and micro-point distribution patterns sampled from the random distribution;
FIG. 7 is a schematic diagram of a high-dimensional random distribution of micro-points in the example;
fig. 8 is a flowchart of a method for authenticating an anti-counterfeit product according to an embodiment of the present invention.
Detailed Description
The invention is further illustrated below with reference to the accompanying drawings and examples.
Fig. 1 is a flow chart of a method for manufacturing an anti-counterfeiting product according to an embodiment of the invention. The manufacturing method of the product comprises the following steps: generating randomly distributed micro-point features (step 101); generating a digital product identification for the product (step 102); embedding the micro-point features into a digital product identification of the product (step 103); the digital product identification embedded with the micro-dot feature is printed on the surface of the product (step 104). Therefore, the surface of the product mark is distributed with the micro-point characteristics which are randomly distributed, and the manufacture of the anti-counterfeiting product is completed.
Fig. 2 is a schematic diagram of embedding a micro-point feature into a product two-dimensional code in an embodiment, wherein the micro-point feature 202 is not shown in detail in the figure due to its small size. In the process of generating the micro-point features, a specific high-dimensional random distribution map 201 of the micro-points is first generated through an algorithm, and as a distribution characteristic of at least one of position distribution, gray scale distribution, color distribution and micro morphology of all the micro-point features, products in the same class or the same batch can commonly follow a certain distribution characteristic, wherein each product has other different micro-point features for distinguishing. For example, different random profiles may be used for different batches of product, and different microdots may be used for different products in the same batch. Then, the random distribution map of the micro-points is sampled by using the algorithm, a micro-point feature 202 with unique identification is generated for each product (or product identification or label), the generated micro-point feature is embedded into a digital two-dimensional identification 203 (such as a quick response matrix code, namely a two-dimensional code) of the product according to a preset avoidance rule, and the two-dimensional code embedded with the micro-point feature is printed on the surface of the product or the surface of a product package to serve as the product identification or the surface of a product label to form a digital product Identification (ID) with the micro-points. The avoidance rules may restrict at least one of a specific position distribution, a gradation distribution, and a color distribution of the micro-points. For example, the position distribution avoidance rule may ensure that only black or dark colored micro-points are generated in the white module of the two-dimensional code, and the gray distribution or color distribution avoidance rule may distribute to ensure that the gray or color of the micro-points satisfies certain gray and saturation limits without interfering with the white module of the two-dimensional code. These avoidance rules work together to ensure that the reading of the two-dimensional code itself is not affected by the embedded micro-point features and that the two-dimensional code still meets the corresponding national and/or international standards after the micro-point features are added. They may have a common distribution of microdots for the same type of product, but their microdots differ in detail (e.g., microscopic morphology).
In some embodiments, the micro-point feature 202 may also be embedded in a black module of the two-dimensional code 203, and the avoidance rule limits the micro-points to be generated only in the black module of the two-dimensional code, so that the two-dimensional code still meets the corresponding national standard and/or international standard after the micro-point feature is added. The white microdots maintain the highest contrast in the black module of the two-dimensional code and are produced by short pauses in the printing ink jet during printing.
The composition of the micro-point features includes the most basic two-dimensional coordinates (X, Y), and may also include other optional features such as color, grayscale, shape, and so forth. Typically, the irreproducibility and anti-counterfeiting performance of the micro-dot feature is achieved first by a random distribution of the two-dimensional positions of the micro-dots. And the color, gray scale or shape characteristics of the micro-points can be used for further improving the anti-counterfeiting performance of the product. Randomly distributed micro-point features may also form randomly distributed micro-point texture features.
In embodiments of the present invention, the size of the micro-dots (e.g., the diameter of the micro-dots or the longest diameter inside the micro-dots) may be set in the range of 50 to 90 micrometers, more preferably in the range of 60 to 80 micrometers. Setting the size of the micro-dots within the above range can make the micro-dots smaller than a size range clearly recognizable by most sampling devices (e.g., high resolution scanners) used in the market. If a two-dimensional code area covered with micro-dots is copied using ordinary sampling equipment, the micro-dots will be lost in the acquired sampled image. Counterfeiters can only copy the two-dimensional code on the product. If counterfeiters use a higher performance scanner to copy product identifiers with embedded microdots, the copying cost is increased and more time is spent. And because the micro-point features are embedded in the two-dimensional code, counterfeit products can still be found through the traceability function of the two-dimensional code. Therefore, the whole anti-counterfeiting system has high-level security.
After the anti-counterfeiting product is manufactured or in the manufacturing process, the micro-point characteristic information on the product mark needs to be stored in a database for subsequent product authenticity verification. The stored information of the characteristics of the micro-points includes, for example, randomly distributed position characteristics, and other characteristics such as color, gray scale, or shape thereof.
Fig. 3(a) to 3(h) are examples of shape vectors of micro-dots in the embodiment, in which eight kinds of micro-dots having different shapes (micro-morphologies) are shown. The lines inside each shape of the microdots are merely exemplary and not necessary, and the color inside may be white, black, or other colors. The shapes of the micro-points illustrated in fig. 3(a) to 3(h) may be encoded as shape vectors shape, respectively1、shape 2、shape 3、shape 4、shape 5、shape 6、shape 7、shape 8The codes may be 000, 001, 010, 011, 100, 101, 110, 111, respectively. For example, a micro-point has position coordinates (X, Y) on the product label, the color (R, G, B) is (0,160,233), and the shape is an angle as shown in fig. 3(f), and it is assumed that the shape is encoded as a shape vector S-shape6Then the micro-point can be characterized as: (X, Y, R, G, B, S).
It will be appreciated by those skilled in the art that the shape vectors of the micro-dots shown in fig. 3(a) to 3(h) are merely exemplary, and that the micro-dots in practical applications may take other shapes.
In an embodiment of the invention, the feature of the micro-points is sampled by a probability density function, that is, the distribution of the positions of the feature or cluster of micro-points in the product identification conforms to a predetermined random distribution. Such random distribution of the micro-points includes a joint distribution of at least one of a uniform distribution, a gaussian distribution, a skewed gaussian distribution, an exponential distribution, or any combination thereof.
Fig. 4(a) and 4(b) show images of probability density functions when a uniform distribution function is employed as a random distribution function of micro-points, and distribution maps of micro-points sampled from the random distribution.
The probability density function of the uniform distribution function is:
PDF(x,y)＝const
in the image of the probability density function of fig. 4(a), the Z-direction coordinate is the probability density, and the lateral coordinate X and the vertical coordinate Y indicate the position (X, Y) of the micro point. The distribution map of the microdots in fig. 4(b) is sampled from the random distribution map in fig. 4(a) when the coordinates (x, y) of the microdots are generated.
Fig. 5(a) and 5(b) show an image of a probability density function when a one-dimensional gaussian distribution function is used as a random distribution function of a micro point, and a distribution map of the micro point sampled from the random distribution.
The probability density function of a one-dimensional gaussian distribution is:
Figure PCTCN2019099544-APPB-000001
in the image of the probability density function of fig. 5(a), the Z-direction coordinate is the probability density, and the lateral coordinate X and the vertical coordinate Y indicate the position (X, Y) of the micro point. The distribution diagram of the microdots in fig. 5(b) is obtained by sampling the random distribution diagram in fig. 5(a) when the coordinates (x, y) of the microdots are generated.
Fig. 6(a) and 6(b) show an image of a probability density function when a two-dimensional gaussian distribution function is used as a random distribution function of a micro point, and a distribution map of the micro point sampled from the random distribution.
The probability density function of a two-dimensional gaussian distribution is:
Figure PCTCN2019099544-APPB-000002
in the image of the probability density function of fig. 6(a), the Z-direction coordinate is the probability density, and the lateral coordinate X and the vertical coordinate Y indicate the position (X, Y) of the micro point. The distribution map of the microdots in fig. 6(b) is sampled from the random distribution map in fig. 6(a) when the coordinates (x, y) of the microdots are generated.
In embodiments of the present invention, an inverse transformation method may be employed to generate the micro-point features. The method comprises the following specific steps:
step 1: firstly, appointing a certain random distribution to obtain a probability density function PDF of the random distribution;
step 2: solving a cumulative distribution function CDF of PDF;
and step 3: inverse transformation is carried out on the CDF to obtain an inverse function CDF thereof-1；
And 4, step 4: generating uniformly distributed random numbers U; and
and 5: substituting U into CDF-1In this way, a random distribution of the micro-point features, i.e., a randomly distributed micro-point group, is obtained.
The random distribution of the micro-points includes, but is not limited to, one-dimensional and high-dimensional forms of uniform distribution, gaussian distribution, skewed gaussian distribution, exponential distribution, etc., and joint distribution obtained by combining them with each other. In the high-dimensional randomly distributed micro-point feature, the micro-point has not only position variation but also variation in gray level. For example, the micro-point feature may conform to the high-dimensional random distribution graph shown in fig. 7, in which three graphs from top to bottom respectively show probability density distributions of X-coordinate, Y-coordinate, and gray scale of the micro-point, the vertical Y-coordinate of the three graphs representing the probability density, and the horizontal X-coordinate indicating the X-coordinate, Y-coordinate, and gray scale of the micro-point, respectively. The gray scale value ranges from 0 to 255.
Fig. 8 is a flowchart of a method for authenticating an anti-counterfeit product according to an embodiment of the present invention. The product verification method comprises the following steps: acquiring an image of a product, and finding an area where a product identifier of the product is located from the acquired image (step 801); reading the micro-point features in the area using image processing techniques (step 802); extracting the micro-point characteristics which are stored in advance and generated aiming at the product (step 803); and comparing the read micro-point characteristics with the pre-stored micro-point characteristics generated for the product to verify the authenticity of the product (step 804).
In the verification process, a single image or a series of image frames of the product identification can be taken by a camera, for example, and then the area where the two-dimensional code is located is accurately found, and the micro-point features are read by using an image processing technology. The two-dimensional code on the product identification plays a role in index, the traceability function of a product transportation chain can be realized by utilizing the two-dimensional code, and the two-dimensional code can be used for extracting and obtaining the micro-point feature record stored in the database in the product manufacturing process. The two-dimensional code region may be used to assist the algorithm in performing image calibration and normalization so that different micro-point features may be compared. Once the micro-point is obtained, it can be verified whether the position distribution, color distribution and high-dimensional distribution characteristics of the micro-point features are consistent with the pre-saved corresponding micro-point features generated for the product. To determine whether the product is genuine or counterfeit.
It will be appreciated by those skilled in the art that the most basic micro-dot feature in embodiments of the present invention is a random distribution of micro-dots, however, other micro-dot features than those described herein may be added to enhance anti-counterfeiting performance and robustness. Point-to-point comparison of finer microdot features with the recorded information saved when the microdots were generated during the product manufacturing process is also possible. If the comparison is the same, it is more certain that the product being verified is authentic. According to the anti-counterfeiting system realized by the embodiment of the invention, the product verification equipment can read the pre-stored micro-point characteristic database remotely in the verification process, so that additional communication overhead is required in the verification process, but the authenticity of the product can be confirmed through remote verification.
It will be understood by those skilled in the art that the various embodiments described above are illustrative only and not restrictive, and that various changes and modifications may be made therein by those skilled in the art without departing from the spirit of the invention and these changes and modifications are intended to be within the scope of the invention.

Claims (13)

  1. An anti-counterfeiting product comprises a product mark; wherein, the surface of the product mark is distributed with micro-point characteristics which are distributed randomly.
  2. The counterfeit-resistant product of claim 1, wherein the product identifier comprises at least one of a bar code and a two-dimensional graphic code.
  3. The security product of claim 1, wherein the microdots of the microdot feature have at least one of a predetermined shape feature, a location feature, a grayscale feature, a color feature.
  4. The security product of claim 1 wherein each of the microdots in the microdot feature is sized in a range of 50 microns to 90 microns.
  5. A method of making an anti-counterfeiting product, comprising:
    generating randomly distributed micro-point features;
    generating a digital product identification for the product;
    embedding the micro-point features into a digital product identification of the product;
    printing the digital product identification embedded with the micro-point features on the surface of the product.
  6. The method of claim 5, wherein the micro-points in the micro-point feature have at least one of a predetermined shape feature, a position feature, a gray scale feature, a color feature.
  7. The method of claim 5, wherein generating randomly distributed micro-point features comprises:
    appointing a random distribution function and obtaining a probability density function PDF of the random distribution function;
    calculating a cumulative distribution function CDF of PDF;
    performing inverse transformation on the CDF to obtain an inverse function CDF of the CDF-1；
    Generating uniformly distributed random numbers U; and
    substituting U into CDF-1In (3), a random distribution of the micro-point features is obtained.
  8. The method of claim 7, wherein the random distribution of the micro-point features comprises a joint distribution of at least one of a uniform distribution, a gaussian distribution, a skewed gaussian distribution, an exponential distribution, or any combination thereof.
  9. The method of claim 7, further comprising:
    and sampling the obtained distribution of the micro-point features, and generating the micro-point features with unique identification for each product or product identification.
  10. The method of claim 5, wherein embedding the micro-dot features into a digital product identification of the product comprises:
    embedding the micro-point characteristics into a digital product identification of the product according to avoidance rules; wherein the avoidance rules limit at least one of a location distribution, a grayscale distribution, and a color distribution of the micro-dots to ensure that the micro-dots do not affect normal reading of the product identification.
  11. The method according to claim 5 or 9, further comprising:
    the generated micro-point features for each product are saved for product verification.
  12. A method of authenticating a counterfeit-resistant product, comprising:
    acquiring an image of the product, and finding an area where a product identifier of the product is located from the acquired image;
    reading the micro-point features in the region using image processing techniques;
    extracting the micro-point characteristics which are pre-stored and generated aiming at the product; and
    and comparing the read micro-point characteristics with pre-stored micro-point characteristics generated for the product to verify the authenticity of the product.
  13. The method of claim 12, wherein the comparing comprises:
    and judging whether at least one of the micro shape, the position distribution, the gray distribution and the color distribution of the read micro point features is consistent with the micro point features which are stored in advance and generated aiming at the product.
CN201980098985.7A 2019-08-07 2019-08-07 Anti-counterfeiting product with random micro-point characteristic and manufacturing method and verification method thereof Pending CN114175048A (en)

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CN105550730A (en) * 2016-01-28 2016-05-04 北京兆信信息技术股份有限公司 Safe two-dimensional code manufacture method and decoding method, and safe two-dimensional code identifier
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CN103208067B (en) * 2013-03-13 2016-08-10 张小北 Antiforge system and the formation of label thereof, embed, understand, differentiate and ownership change method

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Publication number Priority date Publication date Assignee Title
CN101159799A (en) * 1998-07-31 2008-04-09 数字标记公司 Digital watermarking and methods for security documents
CN105550730A (en) * 2016-01-28 2016-05-04 北京兆信信息技术股份有限公司 Safe two-dimensional code manufacture method and decoding method, and safe two-dimensional code identifier
US20190156104A1 (en) * 2017-11-17 2019-05-23 Pixart Imaging Inc. Encoding and decoding method and information recognition device using the same

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